Semiparametric Deconvolution with Unknown Error Variance

Deconvolution is a useful statistical technique for recovering an unknown density in the presence of measurement error. Typically, the method hinges on stringent assumptions about the nature of the measurement error, more specifically, that the distribution is entirely known. We relax this assumption in the context of a regression error component model and develop an estimator for the unknown density. We show semi-uniform consistency of the estimator and provide Monte Carlo evidence that demonstrates the merits of the method

A Laplace Stochastic Frontier Model

We propose a Laplace stochastic frontier model as an alternative to the traditional model with normal errors. An interesting feature of the Laplace model is that the distribution of inefficiency conditional on the composed error is constant for positive values of the composed error, but varies for negative values. Therefore, it may be ideally suited for analyzing industries with many forms on or close to the efficient frontier. A simulation study suggests that the model performs well relative to the normal-exponential model when the two-sided error is misspecified. A brief application to US Airlines is provided

Semiparametric Deconvolution with Unknown Error Variance

Deconvolution is a useful statistical technique for recovering an unknown density in the presence of measurement error. Typically, the method hinges on stringent assumptions about the nature of the measurement error, more specifically, that the distribution is *entirely* known. We relax this assumption in the context of a regression error component model and develop an estimator for the unknown density. We show semi-uniform consistency of the estimator and provide Monte Carlo evidence that demonstrates the merits of the method

Density Deconvolution with Laplace Errors and Unknown Variance

We consider density deconvolution with zero-mean Laplace noise in the context of an error component regression model. We adapt the minimax deconvolution methods of Meister (2006) to allow estimation of the unknown noise variance. We propose a semi-uniformly consistent estimator for an ordinary-smooth target density and a modified “variance truncation device for the unknown noise variance. We provide a simulation study and practical guidance for the choice of smoothness parameters of the ordinary-smooth target density. We apply restricted versions of our estimator to a stochastic frontier model of US banks and to a measurement error model of daily saturated fat intake

A simple method to visualize results in nonlinear regression models

A simple graphical approach to presenting results from nonlinear regression models is described. In the face of multiple covariates, ‘partial mean’ plots may be unattractive. The approach here is portable to a variety of settings and can be tailored to the specific application at hand. A simple four variable nonparametric regression example is provided to illustrate the technique. ► Visualizing estimates from nonlinear models is difficult. ► Propose simple graphical technique to visualize estimates. ► Method had a wide array of application domains. ► Method does not require fixing covariates at a given value

Estimation and inference under economic restrictions

Estimation of economic relationships often requires imposition of constraints such as positivity or monotonicity on each observation. Methods to impose such constraints, however, vary depending upon the estimation technique employed. We describe a general methodology to impose (observation-specific) constraints for the class of linear regression estimators using a method known as constraint weighted bootstrapping. While this method has received attention in the nonparametric regression literature, we show how it can be applied for both parametric and nonparametric estimators. A benefit of this method is that imposing numerous constraints simultaneously can be performed seamlessly. We apply this method to Norwegian dairy farm data to estimate both unconstrained and constrained parametric and nonparametric models

Copper CVD using liquid coinjection of (hfac)Cu(TMVS) and TMVS

Copper chemical vapor deposition using liquid coinjection of the Cu(I) precursor (hfac)Cu(TMVS) along with TMVS has been demonstrated. The coinjection of TMVS with (hfac)Cu(TMVS) stabilizes the Cu precursor until it enters the reaction chamber, allowing for better control of the deposition and faster deposition rates. Using this technique, we have grown films with as-deposited resistivities of 1.86 {plus_minus} 0.04 {mu}{Omega}-cm, independent of film thickness. Deposition rates of well over 100 nm/min are possible. Good step coverage and gap fill down to 0.6 {mu}m lines is demonstrated, with gap fill being limited by the large Cu grain sizes in these films